{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机生成训练集X与对应的标签Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10  -1  -7   8  -3  -3  -2   4 -10  -5  -3   2  -7  -6   5   0  -8   7\n",
      "  -3 -10   3  -6   0   8   5  -1 -10  -9  -4  -2]\n",
      "[-15, 1, -9, 19, -2, -1, 0, 11, -15, -6, -1, 9, -11, -8, 15, 4, -11, 17, -1, -17, 10, -9, 4, 19, 15, 2, -16, -14, -3, 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x205fb490>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "w = 2\n",
    "b = 3\n",
    "xlim = [-10, 10]\n",
    "\n",
    "x_train = np.random.randint(low=xlim[0], high=xlim[1], size=30)\n",
    "y_train = [w*x + b + random.randint(0, 2) for x in x_train]\n",
    "\n",
    "print(x_train)\n",
    "print(y_train)\n",
    "\n",
    "plt.plot(x_train, y_train, 'bo')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、构建线性回归模型\n",
    "\n",
    "y = wx + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3)\n",
      "tensor([-5.1211], grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "# 1. 继承 nn.Module 类\n",
    "\n",
    "\n",
    "class LinearModel(nn.Module):\n",
    "    # 2. 重写 __init__() 方法\n",
    "    #    将所有要学习的参数的层放到构造函数中\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.weight = nn.Parameter(torch.randn(1))\n",
    "        self.bias = nn.Parameter(torch.randn(1))\n",
    "\n",
    "    # 3. 重写 forward() 方法\n",
    "    #    如何向前传播\n",
    "    def forward(self, input):\n",
    "        return (input * self.weight) + self.bias\n",
    "\n",
    "\n",
    "model = LinearModel()\n",
    "x = torch.tensor(3)\n",
    "y = model(x) # 相当于调用 forward 方法\n",
    "print(x)\n",
    "print(y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('weight', Parameter containing:\n",
      "tensor([1.9159], requires_grad=True))\n",
      "('bias', Parameter containing:\n",
      "tensor([3.3171], requires_grad=True))\n"
     ]
    }
   ],
   "source": [
    "model = LinearModel()\n",
    "optimizer = torch.optim.SGD(\n",
    "    model.parameters(), lr=1e-4, weight_decay=1e-2, momentum=0.9)\n",
    "\n",
    "y_train = torch.tensor(y_train, dtype=torch.float32)\n",
    "for _ in range(1000):\n",
    "    # 格式化输入\n",
    "    input = torch.from_numpy(x_train)\n",
    "    # 预测结果\n",
    "    out_put = model(input)\n",
    "    # 获得预测结果与正在结果的损失函数\n",
    "    loss = nn.MSELoss()(out_put, y_train)\n",
    "    # 将梯度清零\n",
    "    model.zero_grad()\n",
    "    # 反向梯度传播\n",
    "    loss.backward()\n",
    "    # 用计算的梯度去做优化\n",
    "    optimizer.step()\n",
    "\n",
    "for parameter in model.named_parameters():\n",
    "    print(parameter)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、模型的保存与加载"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方式一：只保存参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weight', tensor([2.0321])), ('bias', tensor([3.8591]))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只保存参数\n",
    "torch.save(model.state_dict(), './data/temp/linear_model.pth')\n",
    "\n",
    "model.state_dict()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('weight', Parameter containing:\n",
      "tensor([2.0321], requires_grad=True))\n",
      "('bias', Parameter containing:\n",
      "tensor([3.8591], requires_grad=True))\n"
     ]
    }
   ],
   "source": [
    "# 定义网络结构\n",
    "linear_model = LinearModel()\n",
    "# 加载保存的参数\n",
    "linear_model.load_state_dict(torch.load('./data/temp/linear_model.pth'))\n",
    "linear_model.eval()\n",
    "\n",
    "# 输出\n",
    "for parameter in linear_model.named_parameters():\n",
    "    print(parameter)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方式二：保存网络结构与参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存整个模型\n",
    "torch.save(model, './data/temp/linear_model_with_acr.path')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('weight', Parameter containing:\n",
      "tensor([2.0321], requires_grad=True))\n",
      "('bias', Parameter containing:\n",
      "tensor([3.8591], requires_grad=True))\n"
     ]
    }
   ],
   "source": [
    "# 加载模型，不需要创建网络\n",
    "linear_model_2 = torch.load('./data/temp/linear_model_with_acr.path')\n",
    "linear_model_2.eval()\n",
    "\n",
    "# 输出\n",
    "for parameter in linear_model_2.named_parameters():\n",
    "    print(parameter)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、参数微调"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 所有参数进行重新训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AlexNet 网络模型\n",
    "\n",
    "AleNet 属于图像分类的模型，它们都是在 ImageNet 上训练的。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.models as models\n",
    "# 导入 AleNet 网络模型\n",
    "alexnet = models.alexnet(pretrained=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(254)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用图片验证\n",
    "from PIL import Image\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "im = Image.open('./data/img/dog.jpg')\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.RandomResizedCrop((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.485, 0.485], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "input_tensor = transform(im).unsqueeze(0)\n",
    "alexnet(input_tensor).argmax()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "# ImageNet 的类别标签 URL\n",
    "url = \"https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json\"\n",
    "\n",
    "# 发送 HTTP 请求并获取响应\n",
    "response = requests.get(url)\n",
    "\n",
    "# 将响应的 JSON 内容转换为 Python 列表\n",
    "labels = response.json()\n",
    "\n",
    "# 打印索引为 254 的标签\n",
    "print(labels[254])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pug\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# 打开并读取JSON文件\n",
    "with open('./data/imagenet-simple-labels.json.txt', 'r') as f:\n",
    "    labels = json.load(f)\n",
    "print(labels[254])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CIFAR-10数据集\n",
    "\n",
    "CIFAR-10数据集共 60000 张图片，10 个类别，每一类包含 6000 图片。\n",
    "其中 5000 张图片作为训练集，1000 张图片作为测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 32, 32])\n"
     ]
    },
    {
     "data": {
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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=546x70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torchvision\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "cifar10_dataset = torchvision.datasets.CIFAR10(root='./data',\n",
    "                                               train=False,\n",
    "                                               transform=transforms.ToTensor(),\n",
    "                                               target_transform=None,\n",
    "                                               download=False)\n",
    "# 取 32 张图片的 tensor\n",
    "tensor_dataloader = DataLoader(dataset=cifar10_dataset, batch_size=32)\n",
    "\n",
    "data_iter = iter(tensor_dataloader)\n",
    "img_tensor, label_tensor = next(data_iter)\n",
    "print(img_tensor.shape)\n",
    "\n",
    "grid_tensor = torchvision.utils.make_grid(img_tensor, nrow=16, padding=2)\n",
    "grid_img = transforms.ToPILImage()(grid_tensor)\n",
    "display(grid_img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 打印网络结构\n",
    "print(alexnet)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 最后全连接层输入是 4096 个单元，输出是 1000 个单元\n",
    "# 我们将它修改为输出是 10 个单元的全连接层（CIFR10 有 10 类）\n",
    "import torch\n",
    "# 提取分类层的输入参数\n",
    "fc_in_features = alexnet.classifier[6].in_features\n",
    "\n",
    "# 修改预训练模型的输出分类数\n",
    "alexnet.classifier[6] = torch.nn.Linear(fc_in_features, 10)\n",
    "print(alexnet)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在 CIFAR-10 上，使用 AlexNet 作为预训练模型训练我们自己的模型\n",
    "from torch import nn\n",
    "# 1. 数据读入\n",
    "transform = transforms.Compose([\n",
    "    transforms.RandomResizedCrop((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0, 485, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "\n",
    "cifar10_dataset = torchvision.datasets.CIFAR10(root='./data/',\n",
    "                                               train=False,\n",
    "                                               transform=transform,\n",
    "                                               target_transform=None,\n",
    "                                               download=False)\n",
    "\n",
    "dataloader = DataLoader(dataset=cifar10_dataset,  # 传入的数据集，必须参数\n",
    "                        batch_size=32,           # 输出的 batch 大小\n",
    "                        shuffle=True,            # 数据是否打乱\n",
    "                        num_workers=0)           # 进程数，0 表示只有主进程\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer = torch.optim.SGD(\n",
    "    alexnet.parameters(),\n",
    "    lr=1e-4,\n",
    "    weight_decay=1e-2,\n",
    "    momentum=0.9)\n",
    "\n",
    "# 3. 开始训练\n",
    "for epoch in range(3):\n",
    "    for item in dataloader:\n",
    "        out_put = alexnet(item[0])\n",
    "        target = item[1]\n",
    "        # 使用交叉熵损失函数\n",
    "        loss = nn.CrossEntropyLoss()(out_put, target)\n",
    "        print('Epoch {}, Loss {}'.format(epoch+1, loss))\n",
    "\n",
    "        alexnet.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 只训练最后的全连接层\n",
    "\n",
    "固定整个网络的参数，只训练最后的全连接层\n",
    "\n",
    "将全连接层之前的参数全部锁死，让他们无法训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alexnet = models.alexnet()\n",
    "alexnet.load_state_dict(torch.load('C:\\Users\\wangcf\\.cache\\torch\\hub\\checkpoints'))\n",
    "for param in alexnet.parameters():\n",
    "    param.requires_grad = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、训练过程可视化\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 tensorboard\n",
    "\n",
    "对输出的 runs 目录执行：\n",
    "tensorboard --logdir=runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard.writer import SummaryWriter\n",
    "\n",
    "# 创建一个 SummaryWriter 实例\n",
    "writer = SummaryWriter()\n",
    "\n",
    "for n_iter in range(1000):\n",
    "    # 格式化输入\n",
    "    input = torch.from_numpy(x_train)\n",
    "    # 预测结果\n",
    "    out_put = model(input)\n",
    "    # 获得预测结果与正在结果的损失函数\n",
    "    loss = nn.MSELoss()(out_put, y_train)\n",
    "    # 将梯度清零\n",
    "    model.zero_grad()\n",
    "    # 反向梯度传播\n",
    "    loss.backward()\n",
    "    # 用计算的梯度去做优化\n",
    "    optimizer.step()\n",
    "    writer.add_scalar('Loss/train',  # 数据类型的名称，不同名称的数据会使用不同曲线展示\n",
    "                      loss,  # 要保存的数值\n",
    "                      n_iter)  # 训练的 step 数\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 Visdom\n",
    "\n",
    "启动 Visdom： python -m visdom.server\n",
    "\n",
    "打开 127.0.0.1:8097"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from visdom import Visdom\n",
    "import time\n",
    "\n",
    "# 1. 实例化窗口\n",
    "viz = Visdom(port=8097)\n",
    "\n",
    "# 2. 初始化窗口信息\n",
    "viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))\n",
    "\n",
    "for n_iter in range(1000):\n",
    "    # 格式化输入\n",
    "    input = torch.from_numpy(x_train)\n",
    "    # 预测结果\n",
    "    out_put = model(input)\n",
    "    # 获得预测结果与正在结果的损失函数\n",
    "    loss = nn.MSELoss()(out_put, y_train)\n",
    "    # 将梯度清零\n",
    "    model.zero_grad()\n",
    "    # 反向梯度传播\n",
    "    loss.backward()\n",
    "    # 用计算的梯度去做优化\n",
    "    optimizer.step()\n",
    "    viz.line([loss.item()], [n_iter], win='train_loss', update='append')"
   ]
  }
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